class_docstr: Conv1d module definition.
  
  Args:
      input_size: Input dimension.
      output_size: Output dimension.
      kernel_size: Size of the convolving kernel.
      stride: Stride of the convolution.
      dilation: Spacing between the kernel points.
      groups: Number of blocked connections from input channels to output channels.
      bias: Whether to add a learnable bias to the output.
      batch_norm: Whether to use batch normalization after convolution.
      relu: Whether to use a ReLU activation after convolution.
      causal: Whether to use causal convolution (set to True if streaming).
      dropout_rate: Dropout rate.
docstr: Initialize/Reset Conv1d cache for streaming.
  
  Args:
      left_context: Number of previous frames the attention module can see
                    in current chunk (not used here).
      device: Device to use for cache tensor.
docstr_tok: ["Initialize\/Reset","Conv1d","cache","for","streaming",".","Args",":","left_context",":","Number","of","previous","frames","the","attention","module","can","see","in","current","chunk","(","not","used","here",")",".","device",":","Device","to","use","for","cache","tensor","."]
code: def reset_streaming_cache(
      self, left_context: int, device: torch.device
  ) -> None:
      """Initialize/Reset Conv1d cache for streaming.
  
      Args:
          left_context: Number of previous frames the attention module can see
                        in current chunk (not used here).
          device: Device to use for cache tensor.
  
      """
      self.cache = torch.zeros(
          (1, self.input_size, self.kernel_size - 1), device=device
      )
  
file_path: espnet/espnet2/asr_transducer/encoder/blocks/conv1d.py
imports: from typing import Optional, Tuple, Union
  import torch
label_desc: Use image node_id 2 for calling the Conv1d obj's underlying member method code with example usage: obj.reset_streaming_cache(left_context, device) without return types